Preben Mogensen

Bio: Preben Mogensen is an academic researcher from Aalborg University. The author has contributed to research in topics: Telecommunications link & Scheduling (computing). The author has an hindex of 64, co-authored 512 publications receiving 16042 citations. Previous affiliations of Preben Mogensen include Nokia & Bell Labs.

A Site-Specific Study of In-Building Wireless Solutions

Abstract: This paper studies the different ways of extending wireless coverage for high data rates and improving the data capacity in a building. The solutions considered include deployments of single or multiple small base stations, i.e. Picocell or Femtocell, and distributed antenna system, operated on the same frequency as the macro cellular network. We look at the radio performance of each solution in a LTE downlink context with in-building path loss values retrieved from real-life measurements. The performance is compared by means of maximum supportable user numbers and average system throughput. Potential gains by hard frequency reuse in the building and QoS based packet scheduling are examined. Simulation results show the robustness of the proposed distributed antenna system and the conditions for multi-Femtocell deployment.

LTE part II: Radio access [Guest Editorial]

Abstract: Part II of this Feature Topic provides insight into the inner workings of the long term evolution (LTE) air interface.

LTE IoT link budget and coverage performance in practical deployments

Abstract: This paper provides an updated analysis of link-budget and coverage performance for LTE IoT technologies: enhancements for machine type communications (eMTC) and narrowband Internet of Things (NB-IoT). Previous studies have used the 3GPP evaluations assumptions and have demonstrated the coverage capabilities of these technologies when they are independently deployed. Some operators are, however, likely to deploy dual-mode networks — eMTC and NB-IoT — with the intent of supporting different applications in the two systems. With all conditions being equal for the two systems in such a scenario, comparison of the eMTC and NB-IoT technologies then requires that all assumptions are aligned. This paper extends the previous studies with the assumptions aligned for eMTC and NB-IoT systems to provide a fair coverage performance comparison. The study shows that compared to the 3GPP assumption, under the aligned assumptions, both eMTC and NB-IoT can support up to 5dB and 3dB higher uplink coverage, respectively. We further show these link budget improvements yield significantly reduced LTE IoT coverage outage in two simulated scenarios based on real network deployments: a wide area rural case and an urban deep indoor case. The results further demonstrate the potential of these LTE IoT technologies in realistic network deployment conditions.

Performance comparison of vector-RAKE receivers using different combining schemes and antenna array topologies

Abstract: Three potential structures of vector-RAKE (V-RAKE) receivers are compared. An approach where maximal ratio combining (MRC) or conventional beamforming (BF) is applied at each RAKE finger, and a simpler solution where one common BF is used for all fingers. The comparison is conducted by using either single or dual polarized antenna arrays. All results are based on theoretical investigations and analysis of measurement data collected in typical urban environments. It is concluded that the best solution is to use dual polarized antenna arrays, while there is only found to be a minor difference between the considered combining schemes provided the azimuth spread is smaller than the antenna beamwidth.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays

Abstract: Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter/receiver is equipped with, and the more degrees of freedom that the propagation channel can provide, the better the performance in terms of data rate or link reliability. More precisely, on a quasi static channel where a code word spans across only one time and frequency coherence interval, the reliability of a point-to-point MIMO link scales according to Prob(link outage) ` SNR-ntnr where nt and nr are the numbers of transmit and receive antennas, respectively, and signal-to-noise ratio is denoted by SNR. On a channel that varies rapidly as a function of time and frequency, and where circumstances permit coding across many channel coherence intervals, the achievable rate scales as min(nt, nr) log(1 + SNR). The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time [2].

Cognitive Radio An Integrated Agent Architecture for Software Defined Radio

Abstract: An Integrated Agent Architecture for Software Defined Radio. Rapid-prototype cognitive radio, CR1, was developed to apply these.The modern software defined radio has been called the heart of a cognitive radio. Cognitive radio: an integrated agent architecture for software defined radio. Http:bwrc.eecs.berkeley.eduResearchMCMACR White paper final1.pdf. The cognitive radio, built on a software-defined radio, assumes. Radio: An Integrated Agent Architecture for Software Defined Radio, Ph.D. The need for software-defined radios is underlined and the most important notions used for such. Mitola III, Cognitive radio: an integrated agent architecture for software defined radio, Ph.D. This results in the set-theoretic ontology of radio knowledge defined in the. Cognitive Radio An Integrated Agent Architecture for Software.This article first briefly reviews the basic concepts about cognitive radio CR. Cognitive Radio-An Integrated Agent Architecture for Software Defined Radio. Cognitive Radio RHMZ 2007. Software-defined radio SDR idea 1. Cognitive radio: An integrated agent architecture for software.Cognitive Radio SOFTWARE DEFINED RADIO, AND ADAPTIVE WIRELESS SYSTEMS2 Cognitive Networks. 3 Joseph Mitola III, Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio Stockholm.